Condition Monitoring and Control for Intelligent Manufacturing (eBook)

Lihui Wang is a professor of virtual manufacturing at the University of Skövde’s Virtual Systems Research Centre in Sweden. He was previously a senior research scientist at the Integrated Manufacturing Technologies Institute, National Research Council of Canada. He is also an adjunct professor in the Department of Mechanical and Materials Engineering at the University of Western Ontario, and a registered professional engineer in Canada. His research interests and responsibilities are in web-based and sensor-driven real-time monitoring and control, distributed machining process planning, adaptive assembly planning, collaborative design, supply chain management, as well as intelligent and adaptive manufacturing systems. Dr. Robert X. Gao is an Associate Professor of Mechanical Engineering at the University of Massachusetts Amherst, USA. He received his B.S. degree from China, and his M.S. and Ph.D. from the Technical University Berlin, Germany, in 1982, 1985, and 1991, respectively. Since starting his academic career in 1992, he has been conducting research in the general area of embedded sensors and sensor networks, "smart" electromechanical systems, wireless data communication, and signal processing for machine health monitoring, diagnosis, and prognosis. Dr. Gao has published over 100 refereed papers on journals and international conferences, and has one US patent and two pending patent applications on sensing. He is an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, and served as the Guest Editor for the Special Issue on Sensors of the ASME Journal of Dynamic Systems, Measurement, and Control, published in June, 2004. Condition-based Monitoring and Control for Intelligent Manufacturing has arisen from the Flexible Automation and Intelligent Manufacturing (FAIM 2004) conference, held in Toronto, Canada on July12-14 2004. Thirty papers have been selected out of 170 presented at the conference and the authors of these papers have been invited to submit extended updated versions of these papers in order to create a state of the art review of condition-based monitoring and control in manufacturing.

Preface 6
Contents 10
List of Contributors 18
1 Monitoring and Control of Machining --- A. Galip Ulsoy 21
1.1 Introduction 21
1.2 Machining Processes 26
1.3 Monitoring 30
1.3.1 Tool Failure 30
1.3.2 Tool Wear 32
1.4 Servo Control 35
1.5 Process Control 37
1.6 Supervisory Control 43
1.7 Concluding Remarks 45
Acknowledgment 47
References 47
2 Precision Manufacturing Process Monitoring with Acoustic Emission --- D.E. Lee, Inkil Hwang, C.M.O. Valente, J.F.G. Oliveira and David A. Dornfeld 53
2.1 Introduction 53
2.2 Requirements for Sensor Technology at the Precision Scale 55
2.3 Sources of AE in Precision Manufacturing 57
2.4 AE-based Monitoring of Grinding Wheel Dressing 59
2.4.1 Fast AE RMS Analysis for Wheel Condition Monitoring 60
2.4.2 Grinding Wheel Topographical Mapping 61
2.4.3 Wheel Wear Mechanism 62
2.5 AE-based Monitoring of Face Milling 63
2.6 AE-based Monitoring of Chemical Mechanical Planarization 64
2.6.1 Precision Scribing of CMP-treated Wafers 65
2.6.2 AE-based Endpoint Detection for CMP 66
2.7 AE-based Monitoring of Ultraprecision Machining 68
2.7.1 Monitoring of Precision Scribing 68
2.7.2 Monitoring of Ultraprecision Turning of Single Crystal Copper 69
2.7.3 Monitoring of Ultraprecision Turning of Polycrystalline Copper 72
2.8 Conclusions 72
References 73
3 Tool Condition Monitoring in Machining --- Mo A. Elbestawi, Mihaela Dumitrescu and Eu-Gene Ng 75
3.1 Introduction 75
3.2 Research Issues 76
3.2.1 Sensing Techniques 77
3.2.2 Feature Extraction Methods 81
3.2.3 Decision-making Methods 82
3.3 Neural Networks for Tool Condition Monitoring 83
3.3.1 Structure of MPC Fuzzy Neural Networks 84
3.3.2 Construction of MPC Fuzzy Neural Networks 85
3.3.3 Evaluation of MPC Fuzzy Neural Networks 86
3.3.4 Fuzzy Classification and Uncertainties in Tool Condition Monitoring 87
3.4 Case Studies 88
3.4.1 Experimental Tests on MPC Fuzzy Neural Networks for Tool Condition Monitoring 88
3.4.2 Online Monitoring Technique for the Detection of Drill Chipping 95
3.5 Conclusions 98
References 100
4 Monitoring Systems for Grinding Processes --- Bernhard Karpuschewski and Ichiro Inasaki 103
4.1 Introduction to Grinding Processes 103
4.2 Need for Monitoring During Grinding 103
4.3 Monitoring of Process Quantities 104
4.4 Sensors for the Grinding Wheel 111
4.5 Workpiece Sensors 114
4.6 Sensors for Peripheral Systems 119
4.7 Adaptive Control Systems 122
4.8 Intelligent Systems for Abrasive Processes 123
References 126
5 Condition Monitoring of Rotary Machines --- N. Tandon and A. Parey 129
5.1 Introduction 129
5.2 Performance Monitoring 131
5.3 Vibration Monitoring 131
5.3.1 Vibration Signal Processing 138
5.4 Shock Pulse Analysis (SPA) 144
5.5 Current Monitoring 145
5.6 Acoustic Emission Monitoring 146
5.7 Wear Debris and Lubricating Oil Analysis 149
5.7.1 Magnetic Plugs and Chip Detectors 149
5.7.2 Ferrography 149
5.7.3 Particle Counter 152
5.7.4 Spectrographic Oil Analysis (SOA) 153
5.7.5 Lubricating Oil Analysis 153
5.8 Thermography 154
5.9 Conclusions 155
References 155
6 Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings --- Thomas R. Kurfess, Scott Billington and Steven Y. Liang 157
6.1 Introduction 157
6.2 Measurement Basics 158
6.3 Bearing Models 165
6.4 Diagnostics 167
6.4.1 Signal Analysis 167
6.4.2 Effects of Operating Conditions 173
6.4.3 Appropriate Use of Fast Fourier Transforms (FFTs) 177
6.4.4 Trending 177
6.5 Prognostics 178
6.6 Conclusions 183
References 183
7 Sensor Placement and Signal Processing for Bearing Condition Monitoring --- Robert X. Gao, Ruqiang Yan, Shuangwen Sheng and Li Zhang 187
7.1 Introduction 187
7.2 Sensor Placement 189
7.2.1 Structural Attenuation 189
7.2.2 Simulation of Structural Effects 191
7.2.3 Experimental Evaluation 193
7.2.4 Sensor Location Ranking 195
7.3 Signal Processing Techniques 200
7.3.1 Frequency Domain Techniques 200
7.3.2 Time–frequency Techniques 202
7.3.3 Performance Comparison 206
7.4 Conclusions 208
Acknowledgement 209
References 209
8 Monitoring and Diagnosis of Sheet Metal Stamping Processes --- R. Du 213
8.1 Introduction 213
8.2 A Brief Description of Sheet Metal Stamping Processes 214
8.3 Online Monitoring Based on the Tonnage Signal and Support Vector Regression 219
8.3.1 A Study of the Tonnage Signal 219
8.3.2 A Brief Introduction to Support Vector Regression (SVR) 220
8.3.3 Experiment Results 226
8.3.4 Remarks 227
8.4 Diagnosis Based on Infrared Imaging 229
8.4.1 A Study of Diagnosis Methods 229
8.4.2 Thermal Energy and Infrared Imaging 231
8.5 Conclusions 235
Acknowledgments 236
References 237
9 Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling --- Yimin Zhan and Viliam Makis 239
9.1 Introduction 239
9.2 Modeling 241
9.2.1 Noise-adaptive Kalman Filter-based Model 241
9.2.2 Bispectral Feature Energy 244
9.2.3 AR Model Residual-based State Parameter 246
9.2.4 Improved AR Model Residual-based State Parameter 248
9.3 Experimental Set-up 251
9.4 Performance Analysis of BFE 253
9.5 Performance Analysis of MRP 255
9.6 Performance Analysis of IMRP 259
9.7 Conclusions 262
Acknowledgment 263
References 263
10 Signal Processing in Manufacturing Monitoring --- C. James Li 265
10.1 Introduction 265
10.2 Types of Signatures 266
10.3 Signal Processing 267
10.3.1 Time Domain Methods 267
10.3.2 Frequency Domain Methods 271
10.3.3 Time–frequency Methods 276
10.3.4 Model-based Methods 280
10.4 Decision-making Strategy 281
10.4.1 Simple Thresholds 281
10.4.2 Statistical Process Control (SPC) 282
10.4.3 Time/Position-dependent Thresholds 282
10.4.4 Part Signature 282
10.4.5 Waveform Recognition 283
10.4.6 Pattern Recognition 283
10.4.7 Severity Estimator 283
10.5 Conclusions 284
References 284
11 Autonomous Active-sensor Networks for High-accuracy Monitoring in Manufacturing --- Ardevan Bakhtari and Beno Benhabib 287
11.1 Sensor Networks 287
11.1.1 Sensor Fusion 288
11.1.2 Sensor Selection 288
11.1.3 Sensor Modeling 289
11.1.4 An Example of a Multi-sensor Network 290
11.2 Active Sensors 292
11.2.1 Active-sensor Networks for Surveillance of Moving Objects in Static Environments 292
11.2.2 Online Sensor Planning for Surveillance of Dynamic Environments 295
11.3 Agent-based Approach to Online Sensor Planning 296
11.3.1 Agents 296
11.3.2 Advantages and Drawbacks of Multi-agent Systems 297
11.3.3 Examples of Agent-based Sensor-planning Systems 297
11.4 An Active-sensor Network Example for Object Localization in a Multi-object Environment 302
11.4.1 Experimental Set-up 302
11.4.2 Experiments 303
Acknowledgment 306
References 306
12 Remote Monitoring and Control in a Distributed Manufacturing Environment --- Lihui Wang, Weiming Shen, Peter Orban and Sherman Lang 309
12.1 Introduction 309
12.2 WISE-SHOPFLOOR Concept 310
12.3 Architecture Design 312
12.4 Data Collection and Distribution 315
12.4.1 Information Flow 315
12.4.2 Applet–Servlet Communication 315
12.4.3 Sensor Signal Collection and Distribution 316
12.4.4 Virtual Control versus Real Control 317
12.5 Shop Floor Security 318
12.6 Case Study 1: Remote Robot Control 319
12.6.1 Constrained Kinematic Model 320
12.6.2 Inverse Kinematic Model 322
12.6.3 Java 3D Scene-graph Model 323
12.6.4 Remote Tripod Manipulation 325
12.7 Case Study 2: Remote CNC Machining 327
12.7.1 Test Bed Configuration 327
12.7.2 Java 3D Visualization 328
12.7.3 Data Communication 329
12.7.4 Remote Machine Control 329
12.8 Toward Condition-based Monitoring 331
12.9 Conclusions 332
References 333
13 An Intelligent Nanofabrication Probe for Surface Displacement/Profile Measurement --- Wei Gao 335
13.1 Introduction 335
13.2 Design of the Nanofabrication Probe 337
13.2.1 Concept of the Probe 337
13.2.2 Design of the Probe 340
13.3 Evaluation of the Nanofabrication Probe 347
13.3.1 Evaluation of FTC Performance of the Probe 347
13.3.2 Evaluation of Force Detection by the Probe 350
13.3.3 Evaluation of Displacement Detection by the Probe 353
13.4 Nanofabrication and Workpiece Surface Profile Measurement Using the Probe 355
13.5 Conclusions 364
Acknowledgment 364
References 364
14 Smart Transducer Interface Standards for Condition Monitoring and Control of Machines --- Kang B. Lee 367
14.1 Introduction 367
14.2 IEEE 1451 Smart Transducer Interface Standards 369
14.2.1 IEEE 1451.0 - Common Functions and Commands 370
14.2.2 IEEE 1451.1 - Networked Smart Transducer Model 371
14.2.3 IEEE 1451.2 - Transducer-to-Microprocessor Communication Interface 373
14.2.4 IEEE 1451.3 – Distributed Multi-drop Systems for Interfacing Smart Transducers 375
14.2.5 IEEE 1451.4 – Mixed-mode Transducer Interface 376
14.2.6 IEEE P1451.5 – Wireless Transducer Interface 378
14.3 Distributed Control Architecture 379
14.3.1 Networked Smart Sensor Standards 380
14.3.2 Network Communications using Ethernet 380
14.3.3 Distributed Measurement and Control Model 381
14.3.4 Web-based Access to Control Network 383
14.3.5 Internet-based Condition Monitoring 384
14.4 Networked Sensor Application – Machine Tool Condition Monitoring 386
14.4.1 Design Approach 389
14.4.2 System Implementation 389
14.4.3 Hardware System Layout 389
14.5 Conclusions 390
Acknowledgment 391
Disclaimer 391
References 391
15 Rocket Testing and Integrated System Health Management --- Fernando Figueroa and John Schmalzel 393
15.1 Introduction 393
15.2 Background 395
15.3 ISHM for Rocket Test 398
15.3.1 Implementation Strategy 398
15.3.2 DIaK Architecture 398
15.3.3 Object Framework 401
15.4 ISHM Implementation 404
15.4.1 Overall System 404
15.4.2 Intelligent Sensors 405
15.4.3 Process Models 408
15.5 Implementation/Validation: Rocket Engine Test Stand 408
15.6 Conclusions and Future Work 409
Acknowledgments 409
Acronyms 409
References 410
Index 413